In case you have not tried it yet, Calendly has a great Google Analytics integration. It does the job well for basic goal tracking requirements. The setup is pretty much one click, But if you want to dive deeper and track your campaigns, then continue reading the following detailed steps on how to set up an Advanced Calendly Conversion Tracking with Google Tag Manager.
While working with a client who is using our attribution platform I discovered that attribution is quite hard to do with this setup.
So I went on to develop a small solution in Google Tag Manager which allows to connect Calendly events to CRM and Analytics data. It uses the email address of the person registering for an event, hashes it and saves it into the data layer. To understand the path to conversion it then triggers an e-commerce conversion with the hashed email address as transaction id. This allows me to then check what sessions the client id the user had preceding the calendal conversion event.
I can then understand all interactions a user had with the website. A sample visualisation is below:
In it’s approach it is quite similar to what I summarise in the article about how to integrate Google Analytics to any CRM system.
In B2B this is quite important as often the upper funnel touch points are just left out, which in my opinion is a terrible thing to do.
A requirement for this to work is that you use Calendly in an iframe on your website and that you redirect to an external thank you site.
As of this writing the redirect to an external site is only possible if you are on a Calendly pro plan.
So let’s have a look at the configuration I put together to set up an Advanced Calendly Conversion Tracking with Google Tag Manager.
Step 1: Create Calendly page
Create your Calendly page and embed it into your website. Configure the confirmation page dropdown so that the external redirect URL is used. Make sure to tick the box Pass event details to your redirect page.
Step 2: Submit a test lead
Submit a test lead and you will see that you will get your redirect with the event details in the URL. Here is a sample on how it will look like:
https://windsor.ai/thank-youfinished?assigned_to=
SAE%20Australia%20Course%20Advisor%20-%20Silvia&event_type_uuid=GAFVAYKCXPXHXZKG&event_type_name=
meet_matthias&event_start_time=2020-09-17T08%3A45%3A00%2B02%3A00&event_end_time=2020-09-17T09%3A00%3A00%2B02%3A00&invitee_uuid=FDQE2JFXYWQJTWZ2&
invitee_first_name=your&invitee_last_name=name&invitee_email=your.name%40windsor.ai
Step 3: Setup Google Tag Manager
Now we start with the Tag Manager Setup. The way I configured it is the following:
Create a custom variable:
Head to Variables -> User Defined Variable and click New
Select Variable Type URL, Component Type Query. Now enter `invitee_email` as Query Key. This saves the email address from the Calendly pages.
Create a Custom HTML tag:
Head to Tags and click New
Select Tag Type Custom HTML
Now in the HTML Code box you insert
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/sjcl/1.0.8/sjcl.min.js"></script>
<script type="application/javascript">
console.log("windsor calendly email: ", {{calendly_email}});
if ({{calendly_email}} != null)
{
var emails = ({{calendly_email}});
console.log("windsor email: ", emails);
console.log ("length of email: ", emails.length);
var journey_id = sjcl.codec.hex.fromBits(sjcl.hash.sha256.hash(emails));
console.log("windsor journey id was pushed as :", journey_id);
window.dataLayer = window.dataLayer || [];
dataLayer.push({
'transactionId': sjcl.codec.hex.fromBits(sjcl.hash.sha256.hash(emails)),
'transactionTotal': 1
});
}
It will look like this
Head back to Tags and click New again
Select Tag Type Google Analytics: Universal Analytics, Track Type Transaction.
Pick the right Google Analytics Settings variable which links to the property your website is on
Head to Advanced Settings -> Tag Sequencing and select to fire the HTML tag you have just set up before this tag fires.
Step 4: Turn on Google Analytics Ecommerce
Go to Google Analytics and turn on Ecommerce
Step 5: Testing
Now ever lead you submit via Calendly should trigger an Ecommerce transaction with a value of 1 and the hashed email as transaction ID.
To see this head to Ecommerce in Google Analytics and click on Transactions. Now you should see the transactions appearing like this.
Analyzing the data
Google Sheets
To now analyse this data you can load it via Google Analytics Google Sheets plugin which you can get here: https://developers.google.com/analytics/solutions/google-analytics-spreadsheet-add-on. With this, you should be able to see the transaction ID and the last click source / medium / campaign etc.
Google Analytics API
You can query the data directly into your BigQuery project. A good starting point is to get your query set up here: https://ga-dev-tools.appspot.com/dimensions-metrics-explorer/. You can select the metrics and dimensions you want to pull.
Windsor.ai
If you setup your data stream on our platform you will be able to understand the whole conversion path online to form fill. Even better, you can also connect your CRM data so you can understand the whole customer journey from first interaction to becoming your customer.
Wrapping it up
To come back to the beginning of the post where I mentioned the customer which asked me for a solution to the Calendly tracking issue: This solution helps the client not only to track the conversion journeys for the form fills across CRM and Google Analytics data but also for the Calendly form fills. In this particular case the code which was in place was already tracking the other form fills with the hashed emails as transactions.
If you are interested in tracking non Calendly activities such as form submissions in the same way you might want to read the article about integrating Google Analytics with any CRM system I mentioned earlier on in this article.
There are probably other solutions for exactly the same problem out there. In case you have one please feel free to drop us a note or contact us on the chat.
You might also like to read:
Why You Can’t Match Facebook Conversions to Google Analytics Data
Post-View and Post-Click in Attribution: An Overview